{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 神经网络"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 0.引入示例\n",
    "神经网络是一种机器学习模型，试图模仿自然界中生物神经网络的学习模式，可以简单的理解为它在模拟人类大脑的方式在进行学习。生物神经网络具有相互连接的神经元和接收输入的树突，然后基于这些输入，它们通过轴突产生输出信号到另一个神经元。我们将尝试通过使用人工神经网络（ANN）来模仿这个过程，现在我们称其为神经网络。神经网络是深度学习的基础，接下来我们将从使用python来创建单个神经元。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 1.概念"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "    1.1 神经元\n",
    "神经元具有一个或多个输入，偏置，激活功能和单个输出。神经元接收输入，将它们乘以某个权重，然后将它们传递给激活函数(activation function)以产生输出。常用的激活函数有Sigmoid函数、ReLU函数等。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    " \n",
    "def sigmoid(x):\n",
    "    # 直接返回sigmoid函数\n",
    "    return 1. / (1. + np.exp(-x))\n",
    " \n",
    "def plot_sigmoid():\n",
    "    # param:起点，终点，间距\n",
    "    x = np.arange(-10, 10, 0.2)\n",
    "    y = sigmoid(x)\n",
    "    plt.title(\"Sigmod function\")\n",
    "    plt.plot(x, y)\n",
    "    plt.show()\n",
    "    \n",
    "def relu(x):\n",
    "    s = np.where(x < 0, 0, x)\n",
    "    return s\n",
    "\n",
    "def plot_relu():\n",
    "    # param:起点，终点，间距\n",
    "    x = np.arange(-10, 10, 0.2)\n",
    "    y = relu(x)\n",
    "    plt.title(\"ReLU function\")\n",
    "    plt.plot(x, y)\n",
    "    plt.show()\n",
    "\n",
    "#绘制sigmod函数\n",
    "plot_sigmoid()\n",
    "#绘制ReLU函数\n",
    "plot_relu()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "<img src=\"img/pic_1.PNG\" width=400 height=400>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "    1.2 参数调整\n",
    "得到输出后，将它与已知标签进行比较并相应地调整权重（权重通常从随机初始化值开始）。我们不断重复此过程，直到达到允许的最大迭代次数或可接受的错误率。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "    1.3 多层感知器模型\n",
    "为了创建神经网络，我们简单地开始将感知器层添加在一起，创建神经网络的多层感知器模型。您将拥有一个接收数据的输入层和一个将创建结果输出的输出层。其间的任何层都称为隐藏层，因为它们不能直接“看到”您输入的数据或输出的数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "<img src=\"img/pic_2.PNG\" width=300 height=400>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 2.代码实现\n",
    "    2.1 导入相关库文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "    2.2 导入数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "{'data': array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n        1.065e+03],\n       [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n        1.050e+03],\n       [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,\n        1.185e+03],\n       ...,\n       [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n        8.350e+02],\n       [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n        8.400e+02],\n       [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n        5.600e+02]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n       2, 2]), 'target_names': array(['class_0', 'class_1', 'class_2'], dtype='<U7'), 'DESCR': '.. _wine_dataset:\\n\\nWine recognition dataset\\n------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 178 (50 in each of three classes)\\n    :Number of Attributes: 13 numeric, predictive attributes and the class\\n    :Attribute Information:\\n \\t\\t- Alcohol\\n \\t\\t- Malic acid\\n \\t\\t- Ash\\n\\t\\t- Alcalinity of ash  \\n \\t\\t- Magnesium\\n\\t\\t- Total phenols\\n \\t\\t- Flavanoids\\n \\t\\t- Nonflavanoid phenols\\n \\t\\t- Proanthocyanins\\n\\t\\t- Color intensity\\n \\t\\t- Hue\\n \\t\\t- OD280/OD315 of diluted wines\\n \\t\\t- Proline\\n\\n    - class:\\n            - class_0\\n            - class_1\\n            - class_2\\n\\t\\t\\n    :Summary Statistics:\\n    \\n    ============================= ==== ===== ======= =====\\n                                   Min   Max   Mean     SD\\n    ============================= ==== ===== ======= =====\\n    Alcohol:                      11.0  14.8    13.0   0.8\\n    Malic Acid:                   0.74  5.80    2.34  1.12\\n    Ash:                          1.36  3.23    2.36  0.27\\n    Alcalinity of Ash:            10.6  30.0    19.5   3.3\\n    Magnesium:                    70.0 162.0    99.7  14.3\\n    Total Phenols:                0.98  3.88    2.29  0.63\\n    Flavanoids:                   0.34  5.08    2.03  1.00\\n    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\\n    Proanthocyanins:              0.41  3.58    1.59  0.57\\n    Colour Intensity:              1.3  13.0     5.1   2.3\\n    Hue:                          0.48  1.71    0.96  0.23\\n    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\\n    Proline:                       278  1680     746   315\\n    ============================= ==== ===== ======= =====\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML Wine recognition datasets.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\\n\\nThe data is the results of a chemical analysis of wines grown in the same\\nregion in Italy by three different cultivators. There are thirteen different\\nmeasurements taken for different constituents found in the three types of\\nwine.\\n\\nOriginal Owners: \\n\\nForina, M. et al, PARVUS - \\nAn Extendible Package for Data Exploration, Classification and Correlation. \\nInstitute of Pharmaceutical and Food Analysis and Technologies,\\nVia Brigata Salerno, 16147 Genoa, Italy.\\n\\nCitation:\\n\\nLichman, M. (2013). UCI Machine Learning Repository\\n[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\\nSchool of Information and Computer Science. \\n\\n.. topic:: References\\n\\n  (1) S. Aeberhard, D. Coomans and O. de Vel, \\n  Comparison of Classifiers in High Dimensional Settings, \\n  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Technometrics). \\n\\n  The data was used with many others for comparing various \\n  classifiers. The classes are separable, though only RDA \\n  has achieved 100% correct classification. \\n  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \\n  (All results using the leave-one-out technique) \\n\\n  (2) S. Aeberhard, D. Coomans and O. de Vel, \\n  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \\n  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Journal of Chemometrics).\\n', 'feature_names': ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']}\n"
    },
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>alcohol</th>\n      <th>malic_acid</th>\n      <th>ash</th>\n      <th>alcalinity_of_ash</th>\n      <th>magnesium</th>\n      <th>total_phenols</th>\n      <th>flavanoids</th>\n      <th>nonflavanoid_phenols</th>\n      <th>proanthocyanins</th>\n      <th>color_intensity</th>\n      <th>hue</th>\n      <th>od280/od315_of_diluted_wines</th>\n      <th>proline</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>14.23</td>\n      <td>1.71</td>\n      <td>2.43</td>\n      <td>15.6</td>\n      <td>127.0</td>\n      <td>2.80</td>\n      <td>3.06</td>\n      <td>0.28</td>\n      <td>2.29</td>\n      <td>5.64</td>\n      <td>1.04</td>\n      <td>3.92</td>\n      <td>1065.0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>13.20</td>\n      <td>1.78</td>\n      <td>2.14</td>\n      <td>11.2</td>\n      <td>100.0</td>\n      <td>2.65</td>\n      <td>2.76</td>\n      <td>0.26</td>\n      <td>1.28</td>\n      <td>4.38</td>\n      <td>1.05</td>\n      <td>3.40</td>\n      <td>1050.0</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>13.16</td>\n      <td>2.36</td>\n      <td>2.67</td>\n      <td>18.6</td>\n      <td>101.0</td>\n      <td>2.80</td>\n      <td>3.24</td>\n      <td>0.30</td>\n      <td>2.81</td>\n      <td>5.68</td>\n      <td>1.03</td>\n      <td>3.17</td>\n      <td>1185.0</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>14.37</td>\n      <td>1.95</td>\n      <td>2.50</td>\n      <td>16.8</td>\n      <td>113.0</td>\n      <td>3.85</td>\n      <td>3.49</td>\n      <td>0.24</td>\n      <td>2.18</td>\n      <td>7.80</td>\n      <td>0.86</td>\n      <td>3.45</td>\n      <td>1480.0</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>13.24</td>\n      <td>2.59</td>\n      <td>2.87</td>\n      <td>21.0</td>\n      <td>118.0</td>\n      <td>2.80</td>\n      <td>2.69</td>\n      <td>0.39</td>\n      <td>1.82</td>\n      <td>4.32</td>\n      <td>1.04</td>\n      <td>2.93</td>\n      <td>735.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n0    14.23        1.71  2.43               15.6      127.0           2.80   \n1    13.20        1.78  2.14               11.2      100.0           2.65   \n2    13.16        2.36  2.67               18.6      101.0           2.80   \n3    14.37        1.95  2.50               16.8      113.0           3.85   \n4    13.24        2.59  2.87               21.0      118.0           2.80   \n\n   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n0        3.06                  0.28             2.29             5.64  1.04   \n1        2.76                  0.26             1.28             4.38  1.05   \n2        3.24                  0.30             2.81             5.68  1.03   \n3        3.49                  0.24             2.18             7.80  0.86   \n4        2.69                  0.39             1.82             4.32  1.04   \n\n   od280/od315_of_diluted_wines  proline  \n0                          3.92   1065.0  \n1                          3.40   1050.0  \n2                          3.17   1185.0  \n3                          3.45   1480.0  \n4                          2.93    735.0  "
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = datasets.load_wine()\n",
    "print(dataset)\n",
    "data = dataset.data\n",
    "# 为了方便演示将数据和标签一起封装成DataFrame\n",
    "X = pd.DataFrame(data,columns=dataset.feature_names)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "    2.3 检查数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>alcohol</td>\n      <td>178.0</td>\n      <td>13.000618</td>\n      <td>0.811827</td>\n      <td>11.03</td>\n      <td>12.3625</td>\n      <td>13.050</td>\n      <td>13.6775</td>\n      <td>14.83</td>\n    </tr>\n    <tr>\n      <td>malic_acid</td>\n      <td>178.0</td>\n      <td>2.336348</td>\n      <td>1.117146</td>\n      <td>0.74</td>\n      <td>1.6025</td>\n      <td>1.865</td>\n      <td>3.0825</td>\n      <td>5.80</td>\n    </tr>\n    <tr>\n      <td>ash</td>\n      <td>178.0</td>\n      <td>2.366517</td>\n      <td>0.274344</td>\n      <td>1.36</td>\n      <td>2.2100</td>\n      <td>2.360</td>\n      <td>2.5575</td>\n      <td>3.23</td>\n    </tr>\n    <tr>\n      <td>alcalinity_of_ash</td>\n      <td>178.0</td>\n      <td>19.494944</td>\n      <td>3.339564</td>\n      <td>10.60</td>\n      <td>17.2000</td>\n      <td>19.500</td>\n      <td>21.5000</td>\n      <td>30.00</td>\n    </tr>\n    <tr>\n      <td>magnesium</td>\n      <td>178.0</td>\n      <td>99.741573</td>\n      <td>14.282484</td>\n      <td>70.00</td>\n      <td>88.0000</td>\n      <td>98.000</td>\n      <td>107.0000</td>\n      <td>162.00</td>\n    </tr>\n    <tr>\n      <td>total_phenols</td>\n      <td>178.0</td>\n      <td>2.295112</td>\n      <td>0.625851</td>\n      <td>0.98</td>\n      <td>1.7425</td>\n      <td>2.355</td>\n      <td>2.8000</td>\n      <td>3.88</td>\n    </tr>\n    <tr>\n      <td>flavanoids</td>\n      <td>178.0</td>\n      <td>2.029270</td>\n      <td>0.998859</td>\n      <td>0.34</td>\n      <td>1.2050</td>\n      <td>2.135</td>\n      <td>2.8750</td>\n      <td>5.08</td>\n    </tr>\n    <tr>\n      <td>nonflavanoid_phenols</td>\n      <td>178.0</td>\n      <td>0.361854</td>\n      <td>0.124453</td>\n      <td>0.13</td>\n      <td>0.2700</td>\n      <td>0.340</td>\n      <td>0.4375</td>\n      <td>0.66</td>\n    </tr>\n    <tr>\n      <td>proanthocyanins</td>\n      <td>178.0</td>\n      <td>1.590899</td>\n      <td>0.572359</td>\n      <td>0.41</td>\n      <td>1.2500</td>\n      <td>1.555</td>\n      <td>1.9500</td>\n      <td>3.58</td>\n    </tr>\n    <tr>\n      <td>color_intensity</td>\n      <td>178.0</td>\n      <td>5.058090</td>\n      <td>2.318286</td>\n      <td>1.28</td>\n      <td>3.2200</td>\n      <td>4.690</td>\n      <td>6.2000</td>\n      <td>13.00</td>\n    </tr>\n    <tr>\n      <td>hue</td>\n      <td>178.0</td>\n      <td>0.957449</td>\n      <td>0.228572</td>\n      <td>0.48</td>\n      <td>0.7825</td>\n      <td>0.965</td>\n      <td>1.1200</td>\n      <td>1.71</td>\n    </tr>\n    <tr>\n      <td>od280/od315_of_diluted_wines</td>\n      <td>178.0</td>\n      <td>2.611685</td>\n      <td>0.709990</td>\n      <td>1.27</td>\n      <td>1.9375</td>\n      <td>2.780</td>\n      <td>3.1700</td>\n      <td>4.00</td>\n    </tr>\n    <tr>\n      <td>proline</td>\n      <td>178.0</td>\n      <td>746.893258</td>\n      <td>314.907474</td>\n      <td>278.00</td>\n      <td>500.5000</td>\n      <td>673.500</td>\n      <td>985.0000</td>\n      <td>1680.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "                              count        mean         std     min       25%  \\\nalcohol                       178.0   13.000618    0.811827   11.03   12.3625   \nmalic_acid                    178.0    2.336348    1.117146    0.74    1.6025   \nash                           178.0    2.366517    0.274344    1.36    2.2100   \nalcalinity_of_ash             178.0   19.494944    3.339564   10.60   17.2000   \nmagnesium                     178.0   99.741573   14.282484   70.00   88.0000   \ntotal_phenols                 178.0    2.295112    0.625851    0.98    1.7425   \nflavanoids                    178.0    2.029270    0.998859    0.34    1.2050   \nnonflavanoid_phenols          178.0    0.361854    0.124453    0.13    0.2700   \nproanthocyanins               178.0    1.590899    0.572359    0.41    1.2500   \ncolor_intensity               178.0    5.058090    2.318286    1.28    3.2200   \nhue                           178.0    0.957449    0.228572    0.48    0.7825   \nod280/od315_of_diluted_wines  178.0    2.611685    0.709990    1.27    1.9375   \nproline                       178.0  746.893258  314.907474  278.00  500.5000   \n\n                                  50%       75%      max  \nalcohol                        13.050   13.6775    14.83  \nmalic_acid                      1.865    3.0825     5.80  \nash                             2.360    2.5575     3.23  \nalcalinity_of_ash              19.500   21.5000    30.00  \nmagnesium                      98.000  107.0000   162.00  \ntotal_phenols                   2.355    2.8000     3.88  \nflavanoids                      2.135    2.8750     5.08  \nnonflavanoid_phenols            0.340    0.4375     0.66  \nproanthocyanins                 1.555    1.9500     3.58  \ncolor_intensity                 4.690    6.2000    13.00  \nhue                             0.965    1.1200     1.71  \nod280/od315_of_diluted_wines    2.780    3.1700     4.00  \nproline                       673.500  985.0000  1680.00  "
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "count 数量  mean 平均值  std 标准差   min 最小值\n",
    "25% 第一四分位数,等于该样本中所有数值由小到大排列后第25%的数字。\n",
    "50% 中位数\n",
    "75% 同上类似\n",
    "max 最大值\n",
    "\"\"\"\n",
    "X.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(178, 13)"
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]\n[0 1 2]\n"
    }
   ],
   "source": [
    "y=dataset.target\n",
    "print(y)\n",
    "print(np.unique(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "    2.4 将数据集拆分成训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n20     14.06        1.63  2.28               16.0      126.0           3.00   \n40     13.56        1.71  2.31               16.2      117.0           3.15   \n98     12.37        1.07  2.10               18.5       88.0           3.52   \n54     13.74        1.67  2.25               16.4      118.0           2.60   \n145    13.16        3.57  2.15               21.0      102.0           1.50   \n..       ...         ...   ...                ...        ...            ...   \n85     12.67        0.98  2.24               18.0       99.0           2.20   \n12     13.75        1.73  2.41               16.0       89.0           2.60   \n121    11.56        2.05  3.23               28.5      119.0           3.18   \n100    12.08        2.08  1.70               17.5       97.0           2.23   \n9      13.86        1.35  2.27               16.0       98.0           2.98   \n\n     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n20         3.17                  0.24             2.10             5.65  1.09   \n40         3.29                  0.34             2.34             6.13  0.95   \n98         3.75                  0.24             1.95             4.50  1.04   \n54         2.90                  0.21             1.62             5.85  0.92   \n145        0.55                  0.43             1.30             4.00  0.60   \n..          ...                   ...              ...              ...   ...   \n85         1.94                  0.30             1.46             2.62  1.23   \n12         2.76                  0.29             1.81             5.60  1.15   \n121        5.08                  0.47             1.87             6.00  0.93   \n100        2.17                  0.26             1.40             3.30  1.27   \n9          3.15                  0.22             1.85             7.22  1.01   \n\n     od280/od315_of_diluted_wines  proline  \n20                           3.71    780.0  \n40                           3.38    795.0  \n98                           2.77    660.0  \n54                           3.20   1060.0  \n145                          1.68    830.0  \n..                            ...      ...  \n85                           3.16    450.0  \n12                           2.90   1320.0  \n121                          3.69    465.0  \n100                          2.96    710.0  \n9                            3.55   1045.0  \n\n[133 rows x 13 columns]\n-----------\n     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n55     13.56        1.73  2.46               20.5      116.0           2.96   \n29     14.02        1.68  2.21               16.0       96.0           2.65   \n75     11.66        1.88  1.92               16.0       97.0           1.61   \n47     13.90        1.68  2.12               16.0      101.0           3.10   \n64     12.17        1.45  2.53               19.0      104.0           1.89   \n169    13.40        4.60  2.86               25.0      112.0           1.98   \n72     13.49        1.66  2.24               24.0       87.0           1.88   \n11     14.12        1.48  2.32               16.8       95.0           2.20   \n95     12.47        1.52  2.20               19.0      162.0           2.50   \n65     12.37        1.21  2.56               18.1       98.0           2.42   \n93     12.29        2.83  2.22               18.0       88.0           2.45   \n123    13.05        5.80  2.13               21.5       86.0           2.62   \n139    12.84        2.96  2.61               24.0      101.0           2.32   \n26     13.39        1.77  2.62               16.1       93.0           2.85   \n120    11.45        2.40  2.42               20.0       96.0           2.90   \n96     11.81        2.12  2.74               21.5      134.0           1.60   \n160    12.36        3.83  2.38               21.0       88.0           2.30   \n102    12.34        2.45  2.46               21.0       98.0           2.56   \n91     12.00        1.51  2.42               22.0       86.0           1.45   \n141    13.36        2.56  2.35               20.0       89.0           1.40   \n164    13.78        2.76  2.30               22.0       90.0           1.35   \n45     14.21        4.04  2.44               18.9      111.0           2.85   \n7      14.06        2.15  2.61               17.6      121.0           2.60   \n119    12.00        3.43  2.00               19.0       87.0           2.00   \n49     13.94        1.73  2.27               17.4      108.0           2.88   \n92     12.69        1.53  2.26               20.7       80.0           1.38   \n8      14.83        1.64  2.17               14.0       97.0           2.80   \n122    12.42        4.43  2.73               26.5      102.0           2.20   \n66     13.11        1.01  1.70               15.0       78.0           2.98   \n166    13.45        3.70  2.60               23.0      111.0           1.70   \n41     13.41        3.84  2.12               18.8       90.0           2.45   \n3      14.37        1.95  2.50               16.8      113.0           3.85   \n67     12.37        1.17  1.92               19.6       78.0           2.11   \n111    12.52        2.43  2.17               21.0       88.0           2.55   \n30     13.73        1.50  2.70               22.5      101.0           3.00   \n28     13.87        1.90  2.80               19.4      107.0           2.95   \n171    12.77        2.39  2.28               19.5       86.0           1.39   \n152    13.11        1.90  2.75               25.5      116.0           2.20   \n140    12.93        2.81  2.70               21.0       96.0           1.54   \n18     14.19        1.59  2.48               16.5      108.0           3.30   \n81     12.72        1.81  2.20               18.8       86.0           2.20   \n131    12.88        2.99  2.40               20.0      104.0           1.30   \n22     13.71        1.86  2.36               16.6      101.0           2.61   \n112    11.76        2.68  2.92               20.0      103.0           1.75   \n150    13.50        3.12  2.62               24.0      123.0           1.40   \n\n     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n55         2.78                  0.20             2.45         6.250000  0.98   \n29         2.33                  0.26             1.98         4.700000  1.04   \n75         1.57                  0.34             1.15         3.800000  1.23   \n47         3.39                  0.21             2.14         6.100000  0.91   \n64         1.75                  0.45             1.03         2.950000  1.45   \n169        0.96                  0.27             1.11         8.500000  0.67   \n72         1.84                  0.27             1.03         3.740000  0.98   \n11         2.43                  0.26             1.57         5.000000  1.17   \n95         2.27                  0.32             3.28         2.600000  1.16   \n65         2.65                  0.37             2.08         4.600000  1.19   \n93         2.25                  0.25             1.99         2.150000  1.15   \n123        2.65                  0.30             2.01         2.600000  0.73   \n139        0.60                  0.53             0.81         4.920000  0.89   \n26         2.94                  0.34             1.45         4.800000  0.92   \n120        2.79                  0.32             1.83         3.250000  0.80   \n96         0.99                  0.14             1.56         2.500000  0.95   \n160        0.92                  0.50             1.04         7.650000  0.56   \n102        2.11                  0.34             1.31         2.800000  0.80   \n91         1.25                  0.50             1.63         3.600000  1.05   \n141        0.50                  0.37             0.64         5.600000  0.70   \n164        0.68                  0.41             1.03         9.580000  0.70   \n45         2.65                  0.30             1.25         5.240000  0.87   \n7          2.51                  0.31             1.25         5.050000  1.06   \n119        1.64                  0.37             1.87         1.280000  0.93   \n49         3.54                  0.32             2.08         8.900000  1.12   \n92         1.46                  0.58             1.62         3.050000  0.96   \n8          2.98                  0.29             1.98         5.200000  1.08   \n122        2.13                  0.43             1.71         2.080000  0.92   \n66         3.18                  0.26             2.28         5.300000  1.12   \n166        0.92                  0.43             1.46        10.680000  0.85   \n41         2.68                  0.27             1.48         4.280000  0.91   \n3          3.49                  0.24             2.18         7.800000  0.86   \n67         2.00                  0.27             1.04         4.680000  1.12   \n111        2.27                  0.26             1.22         2.000000  0.90   \n30         3.25                  0.29             2.38         5.700000  1.19   \n28         2.97                  0.37             1.76         4.500000  1.25   \n171        0.51                  0.48             0.64         9.899999  0.57   \n152        1.28                  0.26             1.56         7.100000  0.61   \n140        0.50                  0.53             0.75         4.600000  0.77   \n18         3.93                  0.32             1.86         8.700000  1.23   \n81         2.53                  0.26             1.77         3.900000  1.16   \n131        1.22                  0.24             0.83         5.400000  0.74   \n22         2.88                  0.27             1.69         3.800000  1.11   \n112        2.03                  0.60             1.05         3.800000  1.23   \n150        1.57                  0.22             1.25         8.600000  0.59   \n\n     od280/od315_of_diluted_wines  proline  \n55                           3.03   1120.0  \n29                           3.59   1035.0  \n75                           2.14    428.0  \n47                           3.33    985.0  \n64                           2.23    355.0  \n169                          1.92    630.0  \n72                           2.78    472.0  \n11                           2.82   1280.0  \n95                           2.63    937.0  \n65                           2.30    678.0  \n93                           3.30    290.0  \n123                          3.10    380.0  \n139                          2.15    590.0  \n26                           3.22   1195.0  \n120                          3.39    625.0  \n96                           2.26    625.0  \n160                          1.58    520.0  \n102                          3.38    438.0  \n91                           2.65    450.0  \n141                          2.47    780.0  \n164                          1.68    615.0  \n45                           3.33   1080.0  \n7                            3.58   1295.0  \n119                          3.05    564.0  \n49                           3.10   1260.0  \n92                           2.06    495.0  \n8                            2.85   1045.0  \n122                          3.12    365.0  \n66                           3.18    502.0  \n166                          1.56    695.0  \n41                           3.00   1035.0  \n3                            3.45   1480.0  \n67                           3.48    510.0  \n111                          2.78    325.0  \n30                           2.71   1285.0  \n28                           3.40    915.0  \n171                          1.63    470.0  \n152                          1.33    425.0  \n140                          2.31    600.0  \n18                           2.82   1680.0  \n81                           3.14    714.0  \n131                          1.42    530.0  \n22                           4.00   1035.0  \n112                          2.50    607.0  \n150                          1.30    500.0  \n-----------\n[0 0 1 0 2 0 1 1 2 1 1 0 0 1 1 2 0 0 1 1 0 2 0 2 1 2 0 1 1 1 0 1 2 0 1 1 0\n 2 1 1 1 1 2 0 2 1 2 2 0 1 2 0 0 0 0 0 1 1 0 0 1 0 0 0 1 1 2 2 1 1 0 1 0 2\n 2 2 2 1 1 0 1 0 0 1 0 1 0 2 0 0 1 1 0 2 2 1 0 1 1 2 2 1 0 0 2 2 2 0 1 1 2\n 1 2 2 2 2 2 1 1 2 2 2 1 1 1 0 0 2 1 0 1 1 0]\n-----------\n[0 0 1 0 1 2 1 0 1 1 1 1 2 0 1 1 2 1 1 2 2 0 0 1 0 1 0 1 1 2 0 0 1 1 0 0 2\n 2 2 0 1 2 0 1 2]\n"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "print(X_train)\n",
    "print('-----------')\n",
    "print(X_test)\n",
    "print('-----------')\n",
    "print(y_train)\n",
    "print('-----------')\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "    2.5 特征标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "StandardScaler(copy=True, with_mean=True, with_std=True)"
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "[[ 1.34715939 -0.6033837  -0.27884514 ...  0.56991748  1.57776734\n   0.11489291]\n [ 0.72611191 -0.53329634 -0.17008736 ... -0.02881099  1.11487824\n   0.16436655]\n [-0.7519811  -1.09399525 -0.93139182 ...  0.35608588  0.25923477\n  -0.28089619]\n ...\n [-1.75807802 -0.23542504  3.16515125 ... -0.11434363  1.54971345\n  -0.92405348]\n [-1.11218864 -0.20914228 -2.38149556 ...  1.33971123  0.52574667\n  -0.11598406]\n [ 1.09874039 -0.84868948 -0.31509773 ...  0.22778692  1.35333626\n   0.98892719]]\n----------\n[[ 7.26111905e-01 -5.15774497e-01  3.73701547e-01  3.16749698e-01\n   1.18843880e+00  1.05418853e+00  7.53720235e-01 -1.32515968e+00\n   1.46346234e+00  5.19876449e-01  9.94879647e-02  6.23935264e-01\n   1.23629538e+00]\n [ 1.29747559e+00 -5.59579099e-01 -5.32613291e-01 -9.81707111e-01\n  -2.57694415e-01  5.61005812e-01  3.12760743e-01 -8.59105286e-01\n   6.52100649e-01 -1.49366276e-01  3.56085883e-01  1.40944403e+00\n   9.55944761e-01]\n [-1.63386853e+00 -3.84360688e-01 -1.58393850e+00 -9.81707111e-01\n  -1.85387754e-01 -1.09354267e+00 -4.31970845e-01 -2.37699423e-01\n  -7.80729579e-01 -5.37958826e-01  1.16864596e+00 -6.24462593e-01\n  -1.04608846e+00]\n [ 1.14842419e+00 -5.59579099e-01 -8.58886632e-01 -9.81707111e-01\n   1.03838889e-01  1.27691621e+00  1.35146533e+00 -1.24748395e+00\n   9.28308886e-01  4.55111024e-01 -1.99876273e-01  1.04474353e+00\n   7.91032635e-01]\n [-1.00040009e+00 -7.61080273e-01  6.27469701e-01 -1.16069239e-01\n   3.20758871e-01 -6.48087311e-01 -2.55587048e-01  6.16733638e-01\n  -9.87885756e-01 -9.04962901e-01  2.10950499e+00 -4.98220113e-01\n  -1.28686016e+00]\n [ 5.27376711e-01  1.99860971e+00  1.82380529e+00  1.61520651e+00\n   8.99212158e-01 -5.04905231e-01 -1.02971593e+00 -7.81429553e-01\n  -8.49781638e-01  1.49135782e+00 -1.22626794e+00 -9.33055322e-01\n  -3.79843466e-01]\n [ 6.39165258e-01 -5.77100941e-01 -4.23855510e-01  1.32666055e+00\n  -9.08454363e-01 -6.63996431e-01 -1.67395149e-01 -7.81429553e-01\n  -9.87885756e-01 -5.63864996e-01  9.94879647e-02  2.73261709e-01\n  -9.00965786e-01]\n [ 1.42168508e+00 -7.34797511e-01 -1.33834762e-01 -7.50870345e-01\n  -3.30001076e-01 -1.54904590e-01  4.10751741e-01 -8.59105286e-01\n  -5.56829575e-02 -1.98354259e-02  9.12048038e-01  3.29369478e-01\n   1.76401418e+00]\n [-6.27771605e-01 -6.99753829e-01 -5.68865884e-01 -1.16069239e-01\n   4.51454520e+00  3.22369011e-01  2.53966144e-01 -3.93050889e-01\n   2.89629257e+00 -1.05608223e+00  8.69281718e-01  6.28575756e-02\n   6.32716993e-01]\n [-7.51981101e-01 -9.71342366e-01  7.36227482e-01 -3.75760600e-01\n  -1.13081094e-01  1.95096051e-01  6.26331938e-01 -4.67222453e-03\n   8.24730797e-01 -1.92543226e-01  9.97580677e-01 -4.00031518e-01\n  -2.21527824e-01]\n [-8.51348698e-01  4.47926766e-01 -4.96360697e-01 -4.04615196e-01\n  -8.36147702e-01  2.42823411e-01  2.34367944e-01 -9.36781019e-01\n   6.69363664e-01 -1.25037850e+00  8.26515399e-01  1.00266270e+00\n  -1.50124593e+00]\n [ 9.26434740e-02  3.04992017e+00 -8.22634039e-01  6.05295655e-01\n  -9.80761023e-01  5.13278452e-01  6.26331938e-01 -5.48402355e-01\n   7.03889693e-01 -1.05608223e+00 -9.69670027e-01  7.22123860e-01\n  -1.20440410e+00]\n [-1.68196468e-01  5.61818733e-01  9.17490449e-01  1.32666055e+00\n   1.03838889e-01  3.60048506e-02 -1.38248353e+00  1.23813950e+00\n  -1.36767208e+00 -5.43769859e-02 -2.85408912e-01 -6.10435651e-01\n  -5.11773167e-01]\n [ 5.14955761e-01 -4.80730814e-01  9.53743043e-01 -9.52852515e-01\n  -4.74614398e-01  8.79188213e-01  9.10505833e-01 -2.37699423e-01\n  -2.62839135e-01 -1.06189326e-01 -1.57109953e-01  8.90447166e-01\n   1.48366357e+00]\n [-1.89470847e+00  7.12071815e-02  2.28691173e-01  1.72476719e-01\n  -2.57694415e-01  9.58733813e-01  7.63519335e-01 -3.93050889e-01\n   3.93155427e-01 -7.75432051e-01 -6.70305789e-01  1.12890518e+00\n  -3.96334678e-01]\n [-1.44755428e+00 -1.74098594e-01  1.38877416e+00  6.05295655e-01\n   2.48995870e+00 -1.10945179e+00 -1.00031864e+00 -1.79121408e+00\n  -7.29459723e-02 -1.09925918e+00 -2.88109943e-02 -4.56139286e-01\n  -3.96334678e-01]\n [-7.64402051e-01  1.32401882e+00  8.36807985e-02  4.61022677e-01\n  -8.36147702e-01  4.18661054e-03 -1.06891233e+00  1.00511230e+00\n  -9.70622741e-01  1.12435375e+00 -1.69669746e+00 -1.40997136e+00\n  -7.42650144e-01]\n [-7.89243950e-01  1.15011784e-01  3.73701547e-01  4.61022677e-01\n  -1.13081094e-01  4.17823732e-01  9.71805465e-02 -2.37699423e-01\n  -5.04521342e-01 -9.69728326e-01 -6.70305789e-01  1.11487824e+00\n  -1.01310603e+00]\n [-1.21155624e+00 -7.08514749e-01  2.28691173e-01  7.49568634e-01\n  -9.80761023e-01 -1.34808859e+00 -7.45542039e-01  1.00511230e+00\n   4.78951313e-02 -6.24312726e-01  3.98852202e-01  9.09114601e-02\n  -9.73527122e-01]\n [ 4.77692913e-01  2.11381911e-01 -2.50769820e-02  1.72476719e-01\n  -7.63841041e-01 -1.42763419e+00 -1.48047453e+00 -4.67222453e-03\n  -1.66114333e+00  2.39226274e-01 -1.09796899e+00 -1.61573500e-01\n   1.14892915e-01]\n [ 9.99372797e-01  3.86600322e-01 -2.06339949e-01  7.49568634e-01\n  -6.91534380e-01 -1.50717979e+00 -1.30409073e+00  3.06030707e-01\n  -9.87885756e-01  1.95766888e+00 -1.09796899e+00 -1.26970193e+00\n  -4.29317104e-01]\n [ 1.53347363e+00  1.50799815e+00  3.01196360e-01 -1.44923834e-01\n   8.26905497e-01  8.79188213e-01  6.26331938e-01 -5.48402355e-01\n  -6.08099431e-01  8.37892541e-02 -3.70941552e-01  1.04474353e+00\n   1.10436568e+00]\n [ 1.34715939e+00 -1.47815833e-01  9.17490449e-01 -5.20033579e-01\n   1.54997211e+00  4.81460212e-01  4.89144540e-01 -4.70726622e-01\n  -6.08099431e-01  1.75304910e-03  4.41618522e-01  1.39541709e+00\n   1.81348782e+00]\n [-1.21155624e+00  9.73582000e-01 -1.29391775e+00 -1.16069239e-01\n  -9.08454363e-01 -4.73086991e-01 -3.63377146e-01 -4.67222453e-03\n   4.62207486e-01 -1.62601797e+00 -1.14343634e-01  6.51989149e-01\n  -5.97527473e-01]\n [ 1.19810799e+00 -5.15774497e-01 -3.15097730e-01 -5.77742771e-01\n   6.09985515e-01  9.26915573e-01  1.49845182e+00 -3.93050889e-01\n  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1.21012686e+00  8.81237856e-01  1.03811459e+00\n   8.26905497e-01 -9.50360592e-01 -1.06891233e+00  4.61382173e-01\n  -2.45576120e-01  2.43261533e+00 -4.56474191e-01 -1.43802524e+00\n  -1.65457701e-01]\n [ 5.39797661e-01  1.33277974e+00 -8.58886632e-01 -1.73778430e-01\n  -6.91534380e-01  2.42823411e-01  6.55729237e-01 -7.81429553e-01\n  -2.11050091e-01 -3.30709466e-01 -1.99876273e-01  5.81854438e-01\n   9.55944761e-01]\n [ 1.73220883e+00 -3.23034244e-01  5.18711921e-01 -7.50870345e-01\n   9.71518819e-01  2.47010022e+00  1.44945632e+00 -1.01445675e+00\n   9.97360945e-01  1.18911917e+00 -4.13707871e-01  1.21306684e+00\n   2.42366269e+00]\n [-7.51981101e-01 -1.00638605e+00 -1.58393850e+00  5.70583360e-02\n  -1.55921431e+00 -2.98086670e-01 -1.06095517e-02 -7.81429553e-01\n  -9.70622741e-01 -1.58001666e-01  6.98216440e-01  1.25514766e+00\n  -7.75632570e-01]\n [-5.65666857e-01  9.74899432e-02 -6.77623665e-01  4.61022677e-01\n  -8.36147702e-01  4.01914612e-01  2.53966144e-01 -8.59105286e-01\n  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    }
   ],
   "source": [
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "print(X_train)\n",
    "print('----------')\n",
    "print(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "    2.6 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n              beta_2=0.999, early_stopping=False, epsilon=1e-08,\n              hidden_layer_sizes=(300,), learning_rate='constant',\n              learning_rate_init=0.001, max_iter=500, momentum=0.9,\n              n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n              random_state=None, shuffle=True, solver='adam', tol=0.0001,\n              validation_fraction=0.1, verbose=False, warm_start=False)"
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\"\"\"\n",
    "    MLPClassifier(BaseMultilayerPerceptron, ClassifierMixin):\n",
    "        Parameters:\n",
    "            hidden_layer_sizes : 第i个元素表示第i个隐藏层中的神经元数量。\n",
    "            activation : 激活函数\n",
    "            solver : 权重优化的求解器\n",
    "            alpha : 惩罚（正则化项）参数。\n",
    "            batch_size： minibatch的大小\n",
    "            learning_rate：权重更新（学习率）\n",
    "            learning_rate_init：初始的学习率\n",
    "            power_t：反缩放学习率的指数。\n",
    "            max_iter： 最大迭代次数\n",
    "            shuffle： 每次迭代中对样本进行重洗\n",
    "            random_state： 随机种子\n",
    "            tol： 优化的阈值\n",
    "            verbose：是否打印到stdout。\n",
    "            warm_start：重用上一次调用的解决方案以适合初始化\n",
    "            momentum： 梯度下降更新的动量\n",
    "            nesterovs_momentum：是否使用Nesterov动量\n",
    "            early_stopping ：当评价没有改善，是否提前终止训练\n",
    "            validation_fraction：将训练数据留一部分当作早期终止训练的验证集\n",
    "            beta_1：一阶矩向量的指数衰减率\n",
    "            beta_2：二阶矩向量的指数衰减率\n",
    "            epsilon：adam稳定性的价值\n",
    "        Attributes：\n",
    "            classes_：每个输出的类标签。\n",
    "            loss_：使用损失函数计算的当前损失。\n",
    "            coefs_：列表中的第i个元素表示对应于层i的权重矩阵。\n",
    "            intercepts_：列表中的第i个元素表示对应于层i+1的偏置。\n",
    "            n_iter_：迭代次数。\n",
    "            n_layers_：层数。\n",
    "            n_outputs_： 输出的个数。\n",
    "            out_activation_：输出激活函数的名称。\n",
    "            \n",
    "\"\"\"\n",
    "mlp = MLPClassifier(hidden_layer_sizes=(13,13,13),max_iter=500)\n",
    "mlp.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "    2.7 预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = mlp.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "    2.8 生成混淆矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "[[15  0  0]\n [ 0 19  0]\n [ 0  0 11]]\n"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "print(confusion_matrix(y_test,y_pred))"
   ]
  }
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